气候变化领域集成服务门户(CCPortal): Snow depth mapping from stereo satellite imagery in mountainous terrain: Evaluation using airborne laser-scanning data

CCPortal

DOI	10.5194/tc-14-2925-2020
	Snow depth mapping from stereo satellite imagery in mountainous terrain: Evaluation using airborne laser-scanning data
	Deschamps-Berger C.; Gascoin S.; Berthier E.; Deems J.; Gutmann E.; Dehecq A.; Shean D.; Dumont M.
发表日期	2020
ISSN	19940416
起始页码	2925
结束页码	2940
卷号	14 期号:9
英文摘要	Accurate knowledge of snow depth distributions in mountain catchments is critical for applications in hydrology and ecology. Recently, a method was proposed to map snow depth at meter-scale resolution from very-highresolution stereo satellite imagery (e.g., Pléiades) with an accuracy close to 0.5 m. However, the validation was limited to probe measurements and unmanned aircraft vehicle (UAV) photogrammetry, which sampled a limited fraction of the topographic and snow depth variability. We improve upon this evaluation using accurate maps of the snow depth derived from Airborne Snow Observatory laser-scanning measurements in the Tuolumne river basin, USA. We find a good agreement between both datasets over a snow-covered area of 138 km2 on a 3m grid, with a positive bias for a Pléiades snow depth of 0.08 m, a root mean square error of 0.80m and a normalized median absolute deviation (NMAD) of 0.69 m. Satellite data capture the relationship between snow depth and elevation at the catchment scale and also small-scale features like snow drifts and avalanche deposits at a typical scale of tens of meters. The random error at the pixel level is lower in snow-free areas than in snow-covered areas, but it is reduced by a factor of 2 (NMAD of approximately 0.40m for snow depth) when averaged to a 36m grid. We conclude that satellite photogrammetry stands out as a convenient method to estimate the spatial distribution of snow depth in high mountain catchments. © Author(s) 2020.
英文关键词	accuracy assessment; airborne survey; laser method; mapping method; model validation; mountain environment; photogrammetry; Pleiades; satellite imagery; snow; spatial resolution; stereo image; terrain; unmanned vehicle; California; Tuolumne River; United States; United States
语种	英语
来源期刊	Cryosphere
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/202065
作者单位	Centre d'Etudes Spatiales de la Biosphère, CESBIO, Univ. Toulouse, CNES/CNRS/INRA/IRD/UPS, Toulouse, 31401, France; Université Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Etudes de la Neige, Grenoble, 38000, France; Centre National de la Recherche Scientifique (CNRS-LEGOS), Toulouse, 31400, France; National Snow and Ice Data Center, Boulder, CO, United States; Research Applications Lab, National Center for Atmospheric Research (NCAR), Boulder, CO, United States; Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, Switzerland; Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland; Dept. of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States
推荐引用方式 GB/T 7714	Deschamps-Berger C.,Gascoin S.,Berthier E.,et al. Snow depth mapping from stereo satellite imagery in mountainous terrain: Evaluation using airborne laser-scanning data[J],2020,14(9).
APA	Deschamps-Berger C..,Gascoin S..,Berthier E..,Deems J..,Gutmann E..,...&Dumont M..(2020).Snow depth mapping from stereo satellite imagery in mountainous terrain: Evaluation using airborne laser-scanning data.Cryosphere,14(9).
MLA	Deschamps-Berger C.,et al."Snow depth mapping from stereo satellite imagery in mountainous terrain: Evaluation using airborne laser-scanning data".Cryosphere 14.9(2020).